Abstract: Accident location data and present vehicle feature data may be obtained, wherein the accident location data indicates one or more locations associated with one or more vehicular accidents, and the present vehicle feature data indicates one or more sets of vehicle features. A set of prediction model parameters of a prediction model may be configured based on the accident location data and the present vehicle feature data for prediction of damage-reduction-related values associated with a set of available vehicle features. The prediction model may be used to determine one or more damage-reduction-related values based on first vehicle data indicating a first vehicle, a set of vehicle features available to the first vehicle, and a first geospatial location. A first set of features for the first vehicle may be generated based on the damage-reduction-related values.

Abstract: The disclosure describes a system and methods for implementing a recommendation engine. The recommendation engine can at least generate a segmentation identifying a customer group for a product, receive a customer review from a storage location, generate a customer review profile based on the customer review, match the customer review profile to the customer group based on comparing purchase factors, preference levels, or a combination thereof associated with the segmentation and purchase factors, preference levels, or a combination thereof associated with the customer review profile, and recommend the product, one or more features of the product, or a combination thereof to a further customer based on the matched customer review profile.

Abstract: According to certain aspects of the disclosure, a computer-implemented method may be used for providing a vehicle recommendation based on user gestures. The method may include displaying at least one image of a vehicle to a user and receiving at least one gesture from the user performed on the at least one image of the vehicle. Additionally, the method may include assigning a value to the at least one gesture from the user and determining a feature of the vehicle based on the at least one gesture from the user. Additionally, the method may include receiving gesture information related to the at least one gesture and determining a vehicle preference of the user based on the value, the feature of the vehicle, and the gesture information. Additionally, the method may include identifying at least one available vehicle based on the vehicle preference of the user and displaying the at least one available vehicle to the user.

Abstract: A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Abstract: Accident location data and present vehicle feature data may be obtained, wherein the accident location data indicates one or more locations associated with one or more vehicular accidents, and the present vehicle feature data indicates one or more sets of vehicle features. A set of prediction model parameters of a prediction model may be configured based on the accident location data and the present vehicle feature data for prediction of damage-reduction-related values associated with a set of available vehicle features. The prediction model may be used to determine one or more damage-reduction-related values based on first vehicle data indicating a first vehicle, a set of vehicle features available to the first vehicle, and a first geospatial location. A first set of features for the first vehicle may be generated based on the damage-reduction-related values.

Abstract: An augmented reality system including processors and storage devices storing instructions. The instructions may configure the processors to perform operations including determining a location of a mobile device, identifying a facility based on the location, requesting mapping data from a facility server, the mapping data comprising a plurality of vehicles, vehicle location data, and landmarks, identifying one location attribute in a video feed captured by an augmented reality viewer displayed in the mobile device, and determining whether the one location attribute matches the plurality of vehicles or the landmarks. The operations may also include identifying qualified vehicles from the vehicles based on qualification criteria, determining whether the qualified vehicles is in a field of view of the augmented reality viewer based on object attributes and the vehicle location data, and generating a modified video feed by providing an indication associated with qualified vehicles in the field of view.

Abstract: Some embodiments relate to techniques for facilitating training of a prediction model for estimating a threshold score for a user. In some embodiments, a first image of at least a first portion of a first vehicle may be provided to a client device, where the first image may be associated with a first damage score. From the client device, a user-provided score for the first image may be received. Based on the user-provided score, a second image of at least a second portion of a second vehicle may be provided to the client device, where the second image may be associated with a second damage score. Training data may be generated based on the first damage score and the second damage score, and the training data may be provided to a prediction model to train the prediction model to estimate a threshold score for a user.

Abstract: A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Abstract: Accident location data and present vehicle feature data may be obtained, wherein the accident location data indicates one or more locations associated with one or more vehicular accidents, and the present vehicle feature data indicates one or more sets of vehicle features. A set of prediction model parameters of a prediction model may be configured based on the accident location data and the present vehicle feature data for prediction of damage-reduction-related values associated with a set of available vehicle features. The prediction model may be used to determine one or more damage-reduction-related values based on first vehicle data indicating a first vehicle, a set of vehicle features available to the first vehicle, and a first geospatial location. A first set of features for the first vehicle may be generated based on the damage-reduction-related values.

Abstract: A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Abstract: A device obtains image data associated with a document. Using a first machine learning model, the device determines, for the document, a first classification of one of a plurality of document types and a first confidence score associated with the first classification, and a second classification of one of the plurality of document types and a second confidence score associated with the second classification based on the image data. The device determines a difference between the first confidence score and the second confidence score, compares the difference and a threshold value, and accept the first classification of the document when the difference satisfies the threshold value.

Abstract: This invention is directed toward a telescoping vaporizing “pen” or “vape pen” that is small and compact such that it is a discreet way to inhale combustible materials. Because of its size, it looks more like a stick of lip balm or than a vaporizing pen, such that it can be used without attracting unwanted attention. The “pen” also has a unique circuitry that allows for temperatures adequate to vaporize the combustible material being used, but not hot enough to burn the lips or mouth of the user, and has an optional cap over the atomizer top to further provide safely to the user.